Zobrazeno 1 - 10
of 74
pro vyhledávání: '"Liang, Xiaoxuan"'
Autor:
Lavington, Jonathan Wilder, Zhang, Ke, Lioutas, Vasileios, Niedoba, Matthew, Liu, Yunpeng, Green, Dylan, Naderiparizi, Saeid, Liang, Xiaoxuan, Dabiri, Setareh, Ścibior, Adam, Zwartsenberg, Berend, Wood, Frank
The training, testing, and deployment, of autonomous vehicles requires realistic and efficient simulators. Moreover, because of the high variability between different problems presented in different autonomous systems, these simulators need to be eas
Externí odkaz:
http://arxiv.org/abs/2405.04491
Autor:
Green, Dylan, Harvey, William, Naderiparizi, Saeid, Niedoba, Matthew, Liu, Yunpeng, Liang, Xiaoxuan, Lavington, Jonathan, Zhang, Ke, Lioutas, Vasileios, Dabiri, Setareh, Scibior, Adam, Zwartsenberg, Berend, Wood, Frank
Current state-of-the-art methods for video inpainting typically rely on optical flow or attention-based approaches to inpaint masked regions by propagating visual information across frames. While such approaches have led to significant progress on st
Externí odkaz:
http://arxiv.org/abs/2405.00251
Autor:
Niedoba, Matthew, Green, Dylan, Naderiparizi, Saeid, Lioutas, Vasileios, Lavington, Jonathan Wilder, Liang, Xiaoxuan, Liu, Yunpeng, Zhang, Ke, Dabiri, Setareh, Ścibior, Adam, Zwartsenberg, Berend, Wood, Frank
Score function estimation is the cornerstone of both training and sampling from diffusion generative models. Despite this fact, the most commonly used estimators are either biased neural network approximations or high variance Monte Carlo estimators
Externí odkaz:
http://arxiv.org/abs/2402.08018
Despite the remarkable accomplishments of graph neural networks (GNNs), they typically rely on task-specific labels, posing potential challenges in terms of their acquisition. Existing work have been made to address this issue through the lens of uns
Externí odkaz:
http://arxiv.org/abs/2312.13536
Autor:
Niedoba, Matthew, Lavington, Jonathan Wilder, Liu, Yunpeng, Lioutas, Vasileios, Sefas, Justice, Liang, Xiaoxuan, Green, Dylan, Dabiri, Setareh, Zwartsenberg, Berend, Scibior, Adam, Wood, Frank
Simulation of autonomous vehicle systems requires that simulated traffic participants exhibit diverse and realistic behaviors. The use of prerecorded real-world traffic scenarios in simulation ensures realism but the rarity of safety critical events
Externí odkaz:
http://arxiv.org/abs/2309.12508
Autoregressive models based on Transformers have become the prevailing approach for generating music compositions that exhibit comprehensive musical structure. These models are typically trained by minimizing the negative log-likelihood (NLL) of the
Externí odkaz:
http://arxiv.org/abs/2309.09075
Autor:
Martin, Greg, Yang, Pu Justin Scarfy, Bahrini, Aram, Bajpai, Prajeet, Benli, Kübra, Downey, Jenna, Li, Yuan Yuan, Liang, Xiaoxuan, Parvardi, Amir, Simpson, Reginald, White, Ethan Patrick, Yip, Chi Hoi
The goal of this annotated bibliography is to record every publication on the topic of comparative prime number theory (through mid-2024) together with a summary of its results. We use a unified system of notation for the quantities being studied and
Externí odkaz:
http://arxiv.org/abs/2309.08729
The maximum likelihood principle advocates parameter estimation via optimization of the data likelihood function. Models estimated in this way can exhibit a variety of generalization characteristics dictated by, e.g. architecture, parameterization, a
Externí odkaz:
http://arxiv.org/abs/2307.16463
Autor:
Dabiri, Setareh, Lioutas, Vasileios, Zwartsenberg, Berend, Liu, Yunpeng, Niedoba, Matthew, Liang, Xiaoxuan, Green, Dylan, Sefas, Justice, Lavington, Jonathan Wilder, Wood, Frank, Scibior, Adam
When training object detection models on synthetic data, it is important to make the distribution of synthetic data as close as possible to the distribution of real data. We investigate specifically the impact of object placement distribution, keepin
Externí odkaz:
http://arxiv.org/abs/2305.14621
Autor:
Liu, Yunpeng, Lioutas, Vasileios, Lavington, Jonathan Wilder, Niedoba, Matthew, Sefas, Justice, Dabiri, Setareh, Green, Dylan, Liang, Xiaoxuan, Zwartsenberg, Berend, Ścibior, Adam, Wood, Frank
Publikováno v:
2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)
The development of algorithms that learn multi-agent behavioral models using human demonstrations has led to increasingly realistic simulations in the field of autonomous driving. In general, such models learn to jointly predict trajectories for all
Externí odkaz:
http://arxiv.org/abs/2305.11856